A beamforming method for array antenna and array antenna

Abstract

The present invention provides a beamforming method and array antenna for an array antenna, which first determines the beamforming design objectives and constraints of the array antenna in a specified area, and determines the beamforming antenna model and optimization parameters according to the design objectives and constraints, and uses an optimization algorithm to iteratively optimize the amplitude and phase of each array antenna unit in the beamforming antenna model, until the amplitude and phase of each array antenna unit output by the iterative optimization are input into the HFSS simulation model, and the output antenna pattern meets the design objectives. The method disclosed in this embodiment uses amplitude phase beamforming to realize a wide beam with a high roll-off coefficient in the vertical direction under an ultra-large electrical downtilt angle as an inner zone antenna, achieves coverage near HAPs, and solves the problem of dark areas under towers existing in traditional ground base station antennas.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a beamforming method of an array antenna and an array antenna. Background Art

[0002] As the main embodiment and important component of the three-dimensional nature of the marine three-dimensional communication project, the High Altitude Platform Station (HAPS) communication system provides public network services for non-ground environments such as the ocean.

[0003] Because traditional ground base station antennas use multi-sector coverage, when the antenna height reaches 20km, the "darkness under the tower" phenomenon becomes more serious, resulting in coverage holes near HAPs, and therefore cannot meet the coverage requirements of seamless switching with ground communication systems.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, an object of the present invention is to provide a beamforming method for an array antenna and an array antenna to solve the problem of dark areas under the tower existing in the ground base station antenna in the prior art.

[0006] The technical solution of the present invention is as follows:

[0007] In a first aspect, this embodiment provides a beamforming method for an array antenna, which includes:

[0008] Determining beamforming design objectives and constraints for array antennas in a specified area, and constructing an optimization problem based on the beamforming design objectives and constraints;

[0009] Determining a beamforming antenna model and optimization parameters according to the optimization problem;

[0010] The optimization algorithm is used to iteratively optimize the optimization parameters in the beamforming antenna model until the optimization parameters output after the iterative optimization are input into the HFSS simulation model, and the antenna radiation pattern obtained is compared with the beamforming design target to meet the beamforming design target.

[0011] Optionally, the optimization parameters are the amplitude and phase of each array antenna unit.

[0012] Optionally, the step of iteratively optimizing the optimization parameters in the beamforming antenna model by using an optimization algorithm until the optimization parameters output after the iterative optimization are input into the HFSS simulation model, and the antenna pattern obtained is compared with the beamforming design target to meet the beamforming design target includes:

[0013] Optimizing the amplitude and phase of each array antenna unit in the beamforming antenna model using an optimization algorithm;

[0014] Inputting the optimized amplitude and phase into a HFSS simulation model in HFSS simulation software;

[0015] Determine whether the antenna pattern output by the HFSS simulation model meets the beamforming design goal;

[0016] If not, the influence weights of the main lobe and the side lobe in the antenna pattern output by the HFSS simulation model are calculated, and the coefficient weights corresponding to the main lobe and the side lobe in the optimization algorithm are adjusted according to the influence weights;

[0017] According to the adjusted coefficient weights, the optimization algorithm is repeatedly used to optimize the amplitude and phase of each array antenna unit in the beamforming antenna model until it is determined that the antenna radiation pattern output by the HFSS simulation model meets the beamforming design goal.

[0018] Optionally, the step of determining whether the simulated antenna pattern meets the design goal includes:

[0019] It is determined whether the difference between the half-power beamwidth in the horizontal and vertical directions is within a preset angle range, the lower sidelobe level at the specified angle is lower than the preset first level threshold, and the upper sidelobe level is lower than the preset second level threshold.

[0020] Optionally, the step of determining beamforming design objectives and constraints according to the array antenna parameters includes:

[0021] The beamforming design objectives and constraints are determined based on the horizontal and vertical half-power beamwidths of the array antenna in the specified area, the antenna downtilt angle, the lower sidelobe angle and its level, and the upper sidelobe level.

[0022] Optionally, the function expression of the optimization problem is:

[0023] min||f(α,θ)-F target || 2

[0024]

[0025]

[0026] θ∈θ 3dB ;

[0027] Among them, α and θ are the amplitude and elevation angle of the beamforming antenna pattern, respectively. i and α jis the amplitude of any two different array antenna elements in the array antenna, θ i is the phase of any antenna unit in the array antenna, d is the spacing between antenna units in the array antenna, k is the wave number, N is the number of units in the array antenna, θ 3dB is the half-power beamwidth in the vertical direction, and N is a positive integer.

[0028] Optionally, the optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm, or a hybrid optimization algorithm based on the genetic algorithm and the particle swarm optimization algorithm.

[0029] Optionally, the determined beamforming design objectives and constraints are:

[0030]

[0031] Where θ is the elevation angle of the antenna.

[0032] In a second aspect, this embodiment discloses an array antenna, wherein beamforming is performed using the beamforming method of the array antenna.

[0033] Beneficial effect: The present invention provides a beamforming method and array antenna for an array antenna. The antenna array parameters are substituted into the beamforming antenna model by the antenna array parameters, and the amplitude and phase of each array antenna unit in the beamforming antenna model are optimized and iterated by the optimization algorithm, and the optimization iteration results of the amplitude and phase of each array antenna unit are output, and the antenna beam is shaped according to the output optimization iteration results. The method disclosed in this embodiment uses amplitude phase beamforming to realize a wide beam with a high roll-off coefficient in the vertical direction under a super large electrical downtilt angle as an inner zone antenna, to achieve coverage near HAPs, and solve the problem of dark areas under towers existing in traditional ground base station antennas. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary personnel in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0035] Figure 1 is a schematic diagram of the steps of the beamforming method of the array antenna described in this embodiment;

[0036] Figure 2 is a stereogram of the beamforming model described in this embodiment;

[0037] Figure 3is a top view of the beamforming model described in this embodiment;

[0038] Figure 4 is a flowchart of a specific application example of the method described in this embodiment;

[0039] Figure 5 is the array antenna beamforming pattern obtained after the optimization algorithm in this embodiment optimizes the amplitude and phase of the antenna unit;

[0040] Figure 6 This is a simulation diagram of the antenna pattern output by bringing the amplitude-phase relationship into the HFSS simulation model in this embodiment;

[0041] Figure 7 is a waveform diagram of the amplitude and phase of the antenna unit obtained after iterative optimization after adjusting the weights of the optimization algorithm in this embodiment;

[0042] Figure 8 It is a simulation diagram showing that the HFSS simulation results in this embodiment meet the beamforming design objectives. DETAILED DESCRIPTION

[0043] The present invention provides a method for making the purpose, technical solution and effect of the present invention clearer and more specific, and the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] In the embodiments and patent claims, unless otherwise specified, "a", "an" and "the" may refer to a single or a plurality of items.

[0045] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0046] The inventors found that traditional ground base station antennas use a multi-sector coverage method. When the antenna height reaches 20km, the dark area under the tower becomes more serious, resulting in coverage holes near HAPs. The areas with coverage holes cannot achieve normal communication connections due to weak radiation signals. Therefore, it is necessary to design special shaped antennas to achieve wide-area coverage that meets the characteristics of HAPs and seamlessly switches with ground communication systems.

[0047] In order to overcome the above-mentioned problems in the prior art, this embodiment provides a beamforming method and an array antenna for an array antenna, which adopts amplitude phase beamforming to realize a wide beam with a high roll-off coefficient in the vertical direction under an ultra-large electrical downtilt angle as an inner zone antenna, thereby achieving coverage near HAPs and solving the problem of dark areas under towers existing in traditional ground base station antennas.

[0048] Specifically, in this embodiment, a beamforming antenna model is first established, and the amplitude and phase optimization problem of each antenna unit is designed as a problem of solving the optimal solution of the objective function. The optimization problem is iteratively optimized using an optimization algorithm, and the design goal of the antenna pattern is achieved according to the optimization result, thereby obtaining the amplitude and phase of each antenna unit. Specifically, the optimization algorithm can use a genetic algorithm, a particle swarm optimization algorithm, or a hybrid algorithm composed of a genetic algorithm and a particle swarm optimization algorithm. In this embodiment, in order to obtain a better optimization effect, a hybrid algorithm based on a genetic algorithm and a particle swarm optimization algorithm is used.

[0049] The method of the present invention is further analyzed below in conjunction with the method of the embodiment of the present invention and the accompanying drawings.

[0050] This embodiment provides a beamforming method for an array antenna, such as Figure 1 Said, including:

[0051] Step S1: determining the beamforming design objectives and constraints of the array antenna in a specified area, and constructing an optimization problem according to the beamforming design objectives and constraints.

[0052] In this step, the beamforming design objectives and constraints of the array antenna in the specified area are first determined. The specified area in this embodiment is a non-ground environment including but not limited to marine environment, desert, forest and other areas. Specifically, in order to design matching array antenna beamforming for different regional conditions, this step includes:

[0053] The beamforming design objectives and constraints are determined based on one or more of the horizontal and vertical half-power beam widths of the array antenna in the specified area, the antenna downtilt angle, the lower sidelobe angle and its level, and the upper sidelobe level.

[0054] In one implementation, the beamforming design objective and constraint conditions of the array antenna are determined according to the antenna downtilt angle, and the determined beamforming design objective and constraint conditions are:

[0055]

[0056] Where θ is the elevation angle of the antenna.

[0057] An optimization problem is constructed according to the beamforming design objectives and constraints. Specifically, the function expression corresponding to the optimization problem is:

[0058] min||f(α,θ)-F target || 2

[0059]

[0060]

[0061] θ∈θ 3dB

[0062] Among them, α and θ are the amplitude and elevation angle of the beamforming antenna pattern, respectively. i and α j is the amplitude of any two different array antenna elements in the array antenna, θ i is the phase of any antenna unit in the array antenna, d is the spacing between antenna units in the array antenna, k is the wave number, N is the number of units in the array antenna, θ 3dB is the half-power beamwidth in the vertical direction.

[0063] Step S2: determining a beamforming antenna model and optimization parameters according to the optimization problem.

[0064] Combination Figure 2 and Figure 3 As shown, in this step, amplitude phase beamforming is used to shape the uniform coaxial ±45° dual-polarization 5-unit antenna array model to achieve a wide beam with a high roll-off coefficient in the vertical direction under a super-large electrical downtilt angle as an inner zone antenna, which solves the "dark under the tower" problem existing in the ground base station antenna in the prior art in the HAPs application scenario.

[0065] Step S3, using an optimization algorithm to iteratively optimize the optimization parameters in the beamforming antenna model until the optimization parameters obtained after the iterative optimization are input into the HFSS simulation model and the output antenna pattern meets the beamforming design goal.

[0066] An optimization algorithm is used to iteratively optimize the optimization parameters in the beamforming antenna model, and the optimization parameters optimized each time are input into the HFSS simulation model. The antenna pattern simulated according to the input optimization parameters is obtained through the HFSS simulation model, and the simulated antenna pattern is compared with the beamforming design target to determine whether the beamforming design target is met. If so, the optimization is completed; if not, the optimization algorithm is repeatedly used to iteratively optimize the optimization parameters until the antenna pattern simulated by the obtained optimization parameters meets the beamforming design target.

[0067] In one implementation, the optimization parameters are the amplitude and phase of each array antenna element.

[0068] The step of iteratively optimizing the optimization parameters in the beamforming antenna model by using an optimization algorithm until the optimization parameters output after the iterative optimization are input into the HFSS simulation model, and the antenna pattern obtained is compared with the beamforming design target to meet the beamforming design target comprises:

[0069] Optimizing the amplitude and phase of each array antenna unit in the beamforming antenna model using an optimization algorithm;

[0070] Inputting the optimized amplitude and phase into a HFSS simulation model in HFSS simulation software;

[0071] Determine whether the antenna pattern output by the HFSS simulation model meets the beamforming design goal;

[0072] If not, calculating the influence weights of the main lobe and the side lobe in the antenna pattern output by the HFSS simulation model, and adjusting the weights corresponding to the main lobe and the side lobe in the optimization algorithm according to the influence weights;

[0073] According to the adjusted system weights, the optimization algorithm is repeatedly used to optimize the amplitude and phase of each array antenna unit in the beamforming antenna model until it is determined that the antenna radiation pattern output by the HFSS simulation model meets the beamforming design goal.

[0074] The step of determining whether the simulated antenna pattern meets the design target comprises:

[0075] It is determined whether the difference between the half-power beamwidth in the horizontal and vertical directions is within a preset angle range, the lower sidelobe level at the specified angle is lower than the preset first level threshold, and the upper sidelobe level is lower than the preset second level threshold.

[0076] The optimization algorithm is a hybrid optimization algorithm based on genetic algorithm and particle swarm optimization algorithm. The optimization algorithm used in this embodiment is a hybrid optimization algorithm based on genetic algorithm and particle swarm optimization algorithm. By combining genetic algorithm and particle swarm optimization algorithm, a better optimization effect of the amplitude and phase of each array antenna unit in the antenna shaping model is achieved.

[0077] Combine the following Figures 4 to 8 The specific application examples of the method described in the present invention further analyze the method described in the present invention.

[0078] During specific implementation, the inner zone antenna beam design objective function and the parameters to be optimized are determined; in this embodiment, the parameters to be optimized are the amplitude and phase of each array antenna unit.

[0079] The parameters of the antenna beam design objective function include the half-power beam width in the horizontal and vertical directions; the lower sidelobe angle and level; and the upper sidelobe level.

[0080] The beamforming antenna model described in the present invention is a uniform coaxial ±45° dual-polarized 5-unit antenna linear array, usually an antenna unit with ±45° dual linear polarization, including but not limited to dipole antenna, patch antenna, slot coupling antenna; the physical implementation method of the antenna includes but is not limited to PCB board level, board level vibrator, plastic vibrator, etc.

[0081] The objective function is converted into an optimization problem, and the beamforming antenna model is introduced into the optimization algorithm designed by MATLAB, namely, the particle swarm optimization based on the genetic algorithm, and the optimization result is output; Figure 4 This is a schematic diagram of the optimization results. Specifically, the optimization results are the amplitude and phase of each antenna unit. Figure 5 The figure shows the beamforming pattern of the array antenna obtained after the genetic algorithm optimization. The optimization result is brought into the HFSS simulation software to obtain the antenna pattern of the real antenna model output by the HFSS simulation software, as shown in FIG. Figure 6 As shown, it is determined whether the parameters meet the design objectives based on the antenna pattern.

[0082] Specifically, in one embodiment, the basis for judging whether the design target is met is that the difference in half-power beam width in the horizontal and vertical directions is within the range of ±2°; the lower sidelobe level at the specified angle is lower than the target level; and the upper sidelobe level is lower than the target level.

[0083] If the requirements are not met, the main lobe and side lobe influence weights are calculated and brought back to the algorithm to adjust the weights corresponding to the main lobe and side lobe in the optimization algorithm, and iterate again until the design goal of beamforming is met; Figure 7 The figure shows the beamforming pattern of the array antenna obtained after multiple iterations of the optimization algorithm. Figure 8 As shown in the figure, after multiple iterations of optimization parameters based on the optimization algorithm, the antenna radiation pattern that meets the beamforming target design is obtained by simulation using the HFSS simulation model.

[0084] Specifically, when it is determined that the simulated antenna pattern does not meet the design requirements, in order to obtain more accurate optimization parameters, in one implementation, the HFSS model is also used to evaluate the optimization results, and the influence weights of the optimization parameters on the main lobe and side lobe of the beam in the actual model are determined, and the genetic algorithm is brought back to adjust the relevant weights, and it is iterated again until the design goals are met.

[0085] The beamforming method disclosed in this embodiment uses an optimization algorithm (genetic algorithm or particle swarm optimization algorithm or a hybrid algorithm composed of genetic algorithm and particle swarm optimization algorithm) to solve the optimization problem in the antenna amplitude and phase shaping process and brings the influence weights of the main lobe and side lobe in the actual model back to the optimization algorithm for iteration, and integrates the factors affecting the optimization results in the actual model into the influence weights of the main lobe and side lobe and takes them into account in the algorithm design, thereby improving the applicability of the optimization results in the actual model. The beamforming method disclosed in the present invention is suitable for specific wide beams and can be used in 5G ultra-dense coverage scenarios.

[0086] Based on the proposed beamforming method for an array antenna, this embodiment further discloses an array antenna, wherein the array antenna performs a beamforming method using the beamforming method for the array antenna.

[0087] Since the array antenna disclosed in this embodiment adopts the method provided in this embodiment to perform beamforming, a better beamforming effect can be obtained, the dark area under the tower existing in the traditional ground base station antenna can be overcome, and seamless switching coverage with the ground communication system can be achieved, so that areas in non-ground environments can also obtain better communication effects.

[0088] The method and array antenna provided in this embodiment are applicable to high-altitude base station systems (abbreviated as HAPs) that provide uniform wide-area (coverage radius > 100km) seamless wireless coverage for non-ground environments including but not limited to marine environments, deserts, forests, etc. or public emergency events such as emergency disaster relief and epidemic prevention, and can be seamlessly connected to traditional ground communication networks to form an effective supplement. The beamforming method of the array antenna disclosed in the present invention is applicable to specific wide beams and can be used in 5G ultra-dense coverage scenarios.

[0089] The present invention provides a beamforming method and array antenna for an array antenna. The antenna array parameters are collected, the antenna array parameters are substituted into the beamforming antenna model, the amplitude and phase of each array antenna unit in the beamforming antenna model are optimized and iterated using the optimization algorithm, and the optimization iteration results of the amplitude and phase of each array antenna unit are output, and the antenna beam is shaped according to the output optimization iteration results. The method disclosed in this embodiment uses amplitude phase beamforming to realize a wide beam with a high roll-off coefficient in the vertical direction under a super large electrical downtilt angle as an inner zone antenna, to achieve coverage near HAPs, and to solve the problem of dark areas under towers existing in traditional ground base station antennas.

[0090] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A beamforming method for an array antenna, characterized in that: Applied to the ground base station antenna in the HAPs application scenario, the beamforming method includes: Determining beamforming design objectives and constraints for array antennas in a specified area, and constructing an optimization problem based on the beamforming design objectives and constraints; The step of determining the beamforming design target and constraint conditions of the array antenna in the specified area includes: Determine the beamforming design objectives and constraints based on the half-power beamwidth in the horizontal and vertical directions, antenna downtilt angle, lower sidelobe angle and level, and upper sidelobe level of the array antenna in the specified area; Determine the beamforming antenna model and optimization parameters according to the optimization problem; the optimization parameters are the amplitude and phase of each array antenna unit; the beamforming antenna model is a uniform coaxial ±45° dual-polarization 5-unit antenna linear array; Iteratively optimizing the optimization parameters in the beamforming antenna model using an optimization algorithm until the optimization parameters obtained after the iterative optimization are input into the HFSS simulation model and the output antenna pattern meets the beamforming design goal; The step of iteratively optimizing the optimization parameters in the beamforming antenna model by using an optimization algorithm until the optimization parameters obtained after the iterative optimization are input into the HFSS simulation model and the output antenna pattern meets the beamforming design goal comprises: Optimizing the amplitude and phase of each array antenna unit in the beamforming antenna model using an optimization algorithm; Inputting the optimized amplitude and phase into a HFSS simulation model in HFSS simulation software; Determine whether the antenna pattern output by the HFSS simulation model meets the beamforming design goal; If not, the influence weights of the main lobe and the side lobe in the antenna pattern output by the HFSS simulation model are calculated, and the coefficient weights corresponding to the main lobe and the side lobe in the optimization algorithm are adjusted according to the influence weights; Repeating the step of optimizing the amplitude and phase of each array antenna unit in the beamforming antenna model using an optimization algorithm according to the adjusted coefficient weights until it is determined that the antenna pattern output by the HFSS simulation model meets the beamforming design goal; The step of judging whether the antenna pattern output by the HFSS simulation model meets the beamforming design goal comprises: Determine whether the difference between the half-power beamwidth in the horizontal and vertical directions is within a preset angle range, the lower sidelobe level at the specified angle is lower than a preset first level threshold, and the upper sidelobe level is lower than a preset second level threshold; The functional expression of the optimization problem is: ； in, are the amplitude and elevation angle of the beamforming antenna pattern, and is the amplitude of any two different array antenna elements in the array antenna, is the phase of any antenna element in the array antenna, d is the spacing between antenna elements in the array antenna, k is the wave number, N is the number of elements in the array antenna, is the half-power beamwidth in the vertical direction, and N is a positive integer; The determined beamforming design objectives and constraints are: ； in, θ is the elevation angle of the antenna.

2. The beamforming method of array antenna according to claim 1, characterized in that: The optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm, or a hybrid optimization algorithm based on the genetic algorithm and the particle swarm optimization algorithm.

3. An array antenna, characterized in that: Beamforming is performed using the beamforming method for an array antenna as described in any one of claims 1 to 2.

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